Early Fire Detection Based On Smoke Segmentation And Smoke Diffusion

The traditional contact fire detection method can achieve good detection results in some narrow closed scenes,but it is difficult to achieve effective effects in an open environment.For the non-contact fire detection scheme based on video analysis,it has a wide application scenario,which can effectively overcome the shortcomings of the existing contact detection.At present,video-based fire detection research still has many shortcomings,such as the lack of a unified standard fire detection video database,low identification accuracy of complex scenes,and great influence by external factors such as lighting and environment.This paper is based on cabin fire detection project co-operated by 701 Research Institute of China shipbuilding Industry Corporation and Chongqing University.By analyzing the characteristics of smoke in the fire generation process,a complete set of early fire detection methods is proposed to solve some shortcomings of the current detection methods,and provide a certain technical direction for the subsequent development of video-based fire detection methods.Firstly,this paper proposes a pixel-adaptive smoke foreground separation method based on multi-connected components to analyze the moving objects in the video to extract the foreground regions in the image.This method firstly carries out the pre-processing work of denoising,greying and enhancing the video frame to enhance the smoke contrast in the video frame.Then,Sobel operator is used for edge detection.According to the edge information,N video frames of different connected regions are calculated to quickly establish N background models.Finally,the background model is used to extract the foreground of the suspected smoke region in video frames.Secondly,the edge contour of the suspected smoke foreground area is expressed as Freeman chain code,and the target area is extracted from the original image according to the chain code.Then the target area is enlarged to 256*256 size by bilinear difference method.Then the image is divided into pixel blocks of the same size,and the smoke is identified by the Bayesian classifier trained by the pure smoke sample and smokeless sample to determine whether each pixel block is a smoke pixel block.At last,the smoke in the original image was judged according to the smoke in each pixel block.Finally,if there is fire smoke in the current video frame,the M frame image after the current video frame is extracted to analyze the diffusing property of early fire smoke,so as to distinguish the fire-like smoke and fire smoke and comprehensively judge whether there is fire in the scene under the current video frame.At the same time in order to verify the validity of the algorithm,to calculate the effect of different algorithms in different scenarios,and then through the detection rate and false alarm rate and missing alarm rate compared the effectiveness of the algorithm.The results show that the detection method proposed in this paper can effectively detect the fire in various scenarios,and can achieve good accuracy.